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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "NeonSoftmaxFloatWorkload.hpp"
#include "NeonWorkloadUtils.hpp"
#include <aclCommon/ArmComputeUtils.hpp>
#include <arm_compute/runtime/NEON/functions/NESoftmaxLayer.h>
namespace armnn
{
NeonSoftmaxFloatWorkload::NeonSoftmaxFloatWorkload(const SoftmaxQueueDescriptor& descriptor,
const WorkloadInfo& info, std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager)
: FloatWorkload<SoftmaxQueueDescriptor>(descriptor, info)
{
m_Data.ValidateInputsOutputs("NeonSoftmaxFloatWorkload", 1, 1);
// The ArmCompute softmax layer uses 2D input/output tensors, so flatten the first three dimensions.
arm_compute::ITensor& input = boost::polymorphic_downcast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
arm_compute::ITensor& output = boost::polymorphic_downcast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
auto layer = std::make_unique<arm_compute::NESoftmaxLayer>(memoryManager);
unsigned int aclAxis = ComputeSoftmaxAclAxis(m_Data.m_Parameters, info.m_InputTensorInfos[0]);
layer->configure(&input, &output, m_Data.m_Parameters.m_Beta, aclAxis);
m_SoftmaxLayer.reset(layer.release());
}
void NeonSoftmaxFloatWorkload::Execute() const
{
ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonSoftmaxFloatWorkload_Execute");
m_SoftmaxLayer->run();
}
} //namespace armnn
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